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Edited
Jul 26, 2023
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Data science workflows in JavaScript Course
HTL-MAR-FiddlerCrabBodySize.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
crabs
Object.keys(crabs[0])
crabsJS = crabs.map(d => ({lat: d.Latitude,
site: d["Site "],
sizeMm: d.carapace_width,
airTempC: d.MATA,
waterTempC: d.MATW}))
crabs
X
carapace_width
Y
—
Color
#c83dac
Size
—
Facet X
—
Facet Y
Site
Mark
Auto
Type Chart, then Shift-Enter. Ctrl-space for more options.
Plot.plot({
marks: [
Plot.rectY(
crabs,
Plot.binX(
{ y: "count" },
{fy: "Site ", x: "carapace_width", fill: "red", tip: true }
)
),
Plot.frame({strokeOpacity: 0.1})
]
})
Plot.plot({
marks: [
Plot.dot(crabsJS, {y: "lat", x: "sizeMm", fill: "site", opacity: 0.5, r: 5}),
Plot.frame({stroke: "#ccc"})
],
height: 500,
inset: 20,
y: {ticks: 5},
color: {legend: true}
})
Plot.plot({
marks: [
Plot.dot(crabsJS, {y: "sizeMm", x: "waterTempC", fill: "gray", opacity: 0.5, r: 5}),
Plot.frame({stroke: "#ccc"})
],
height: 500,
inset: 20,
y: {ticks: 5},
grid: true
})
ML = require("https://www.lactame.com/lib/ml/6.0.0/ml.min.js")
crabsLM = new ML.SimpleLinearRegression(crabsJS.map(d => d.lat), crabsJS.map(d => d.sizeMm))
crabsLM.slope
crabsLM.intercept
crabsLM.predict(36)
Plot.plot({
marks: [
Plot.dot(crabsJS, {x: "lat", y: "sizeMm", fill: "steelblue", opacity: 0.6, r: 4}),
Plot.dot(crabsJS, Plot.groupX({ y: "mean" }, { x: "lat", y: "sizeMm", tip: true, fill: "orange", symbol: "square", r: 4})),
Plot.linearRegressionY(crabsJS, {x: "lat", y: "sizeMm"}),
Plot.frame()
],
x: {label: "Latitude (degrees)", domain: [28, 44]},
y: {label: "Carapace size (millimeters)", domain: [5, 25], ticks: 5, grid: true}
})
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